This paper is an outcome of an international collaborative research initiative. Researchers from 24 institutions across 12 countries were invited to discuss the state-of-the-art in railway train air brake modelling with an emphasis on freight trains. Discussed models are classified as empirical, fluid dynamics and fluid-empirical dynamics models. Empirical models are widely used, and advanced versions have been used for train dynamics simulations. Fluid dynamics models are better models to study brake system behaviour but are more complex and slower in computation. Fluid-empirical dynamics models combine fluid dynamics brake pipe models and empirical brake valve models. They are a balance of model fidelity and computational speeds. Depending on research objectives, detailed models of brake rigging, friction blocks and wheel-rail adhesion are also available. To spark new ideas and more research in this field, the challenges and research gaps in air brake modelling are discussed.

The hybrid energy storage system (HESS), which is composed of battery and ultracapacitor, is established to enhance the performance of an electric vehicle (EV). Moreover, several studies highlight the gains of split the traction power demand among electric motors (EMs) with different characteristics and drivetrain configurations, allowing these to operate at higher efficiency. In this paper, we unite the advantages of the multi EM drive train with the HESS power split and propose a novel two HESS system, in which each HESS is in charge of one of the drivetrain configurations (front and rear system). The EV is driven by two in-wheel EM at the front wheels and a single EM assembled with a differential transmission that moves the rear wheels. In this paper, a multiobjective optimization, based on a genetic algorithm (GA), is formulated to minimize the HESS sizing and maximize the driving range of the vehicle. Also employing the fuzzy control, this strategy is responsible to split the power between the front and rear wheels systems in a more reasonable way to satisfy the demands of better performance. The complete strategy has been developed under the FTP-75 (urban), HWFET (highway) and the US06 (high speed and required acceleration) driving cycles using the MATLAB/Simulink software environment. As compared to a similar EV with a single HESS system, the proposed dual-HESS configuration was able to improve the driving range in 145.15 km also decreasing 23.93% of the HESS mass.